Microtubule-Based Intracellular Transport in Neurons and Fibroblasts: Experimental Studies, Data Analysis, and Mathematical Modelling

Abstract

Neuronal transport plays an important role in cellular function, relying on the movement of kinesins and dyneins. While the structural aspects of these motor proteins are well described, their precise mechanisms in organelle movement remain unclear. In this study, we investigated the role of mutations in kinesin light chain 1 (KLC1), kinesin light chain 2 (KLC2), and kinesin heavy chain (KHC) on the transport properties of dense core vesicles (DCVs) in C. elegans. We found surprising results by crossing these mutant strains with the ida-1::gfp strain, which has green fluorescent protein attached to DCVs and imaged their movement. We show that the KLC1 null mutation (klc-1(ok2609)) had no effect on anterograde transport but increased the speed of retrograde transport and extended the lifespan of the worms expressing IDA1::GFP. Additionally, the KLC2 mutation (klc-2(km11)) decreased DCV movement in both directions, prolonged worm lifespan, and reduced the frequency of body bends during swimming. Finally, the KHC mutation (unc-116(rh24sb79)) significantly impaired transport in both directions, shortened lifespan and greatly diminished swimming ability. Our findings suggest that kinesin-1 not only influences anterograde and retrograde transport but also plays a crucial role in regulating the lifespan and behaviour of the whole animal. We conducted a mathematical analysis of this data and discovered that DCVs exhibit superdiffusive movement with displacement variance var(x) ~ t^2 across strains with KLC mutations and worms without mutations, characterized by low reversal rates and frequent immobilization. The distribution of DCV displacements fits a beta-binomial distribution, driving us to propose a heterogeneous random walk model to explain the superdiffusive retrograde transport behaviour. Furthermore, we demonstrate the anomalous nature of endosome transport along microtubules and develop a neural network to segment endosome trajectories into persistent and antipersistent movements. We introduce a novel persistent random walk model with a rest state for the stochastic transport of endosomes and validate its applicability using real biological data.

Date of Award	1 Aug 2025
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Sergei Fedotov (Supervisor) & Viki Allan (Supervisor)